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"""GPT Blocks used for the GPT Model."""
from typing import Any, Optional
import torch
import torch.nn as nn
from .fc import FC_CLASS_REGISTRY
try:
import transformer_engine.pytorch as te
except:
te = None
class MPTMLP(nn.Module):
def __init__(self, d_model: int, expansion_ratio: int, fc_type: str='torch', device: Optional[str]=None, bias: bool=True):
super().__init__()
fc_kwargs: dict[str, Any] = {'bias': bias}
if fc_type != 'te':
fc_kwargs['device'] = device
self.up_proj = FC_CLASS_REGISTRY[fc_type](d_model, expansion_ratio * d_model, **fc_kwargs)
self.act = nn.GELU(approximate='none')
self.down_proj = FC_CLASS_REGISTRY[fc_type](expansion_ratio * d_model, d_model, **fc_kwargs)
self.down_proj._is_residual = True
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.down_proj(self.act(self.up_proj(x)))
FFN_CLASS_REGISTRY = {'mptmlp': MPTMLP}
if te is not None:
te.LayerNormMLP._has_norm = True
FFN_CLASS_REGISTRY['te_ln_mlp'] = te.LayerNormMLP
def build_ffn(d_model: int, expansion_ratio: int, fc_type: str='torch', device: Optional[str]=None, bias: bool=True, **kwargs: Any) -> nn.Module:
ffn_type = kwargs.pop('ffn_type')
if ffn_type == 'mptmlp':
if len(kwargs) > 0:
raise ValueError(f'MPTMLP got an unexpected keyword argument: {kwargs}')
return MPTMLP(d_model=d_model, expansion_ratio=expansion_ratio, fc_type=fc_type, device=device, bias=bias)
elif ffn_type == 'te_ln_mlp':
assert te is not None
return te.LayerNormMLP(hidden_size=d_model, ffn_hidden_size=d_model * expansion_ratio, bias=bias, **kwargs)
raise ValueError(f'ffn_type={ffn_type!r} not recognized.') |